PDF(Pubmed)
Abstract：
Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches.
摘要：
通风模式是最关键的呼吸机设置之一，由重症监护病房的知识渊博的重症监护治疗师选择和设置。特定通气模式的应用必须是患者特异性的和患者交互式的。这项研究的主要目的是提供有关通气模式设置的详细概述，并确定最佳的机器学习方法，以创建可部署的模型，以便在每次呼吸的基础上适当选择通气模式。利用每次呼吸患者数据，预处理，最后创建一个由五个特征列(吸气和呼气潮气量，最小压力,呼气末正压,和先前的呼气末正压）和一个输出列（输出列包括要预测的模式）。数据帧已分为训练和测试数据集，测试大小为30％。六种机器学习算法进行了训练和性能比较，基于准确性，F1得分,灵敏度,和精度。输出表明，随机森林算法在正确预测所有通风模式方面是最精确和准确的。在所有训练过的机器学习算法中。因此，随机森林机器学习技术可用于预测最佳通风模式设置，如果在最相关的数据的帮助下进行了适当的培训。除了通风模式，控制参数设置，警报设置和其他设置也可以调整机械通风过程利用适当的机器学习，特别是深度学习方法。
